A set of esophageal cancer methylation early screening markers and application thereof

Abstract

The application belongs to the technical field of biological pharmacy, and provides a set of esophageal cancer methylation early screening markers and application thereof, the markers being any one or a combination of two or more of ZNF693, MMP14, JAG1, RUNX1 or NOTCH1 gene sequences or gene fragments containing at least one CpG methylation site; the application also discloses application of the set of biomarkers in preparation of a detection kit for diagnosing and / or evaluating the methylation degree of esophageal cancer or in screening of drugs for treating and / or relieving esophageal cancer. The application can effectively identify cancer patients by quantifying the dynamic change of the methylation level, and reduce the risk of missed detection caused by tumor heterogeneity; by establishing a high-throughput and standardized detection process, the application can provide a precise molecular typing tool for early intervention of esophageal cancer; the application overcomes the limitations of traditional technology, such as insufficient detection sensitivity for early cancer and dependence on invasive operation, and can promote clinical transformation of "early screening and early diagnosis".

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biotechnology, and particularly relates to a set of methylation early screening markers for esophageal cancer and application thereof. BACKGROUND

[0002] Esophageal cancer is mainly squamous cell carcinoma (accounting for 90%), which is closely related to long-term consumption of overcooked food, nitrosamine exposure and genetic factors. Although early esophageal cancer can achieve more than 90% 5-year survival rate through endoscopic resection, 70%-80% of the existing patients are in the middle and advanced stages when diagnosed, and the 5-year survival rate is only 15%-20%, which is mainly due to the insufficient early screening coverage. As can be seen, in the diagnosis and treatment of esophageal cancer, early screening and accurate diagnosis are the key to improve the prognosis, but the existing technology still has significant limitations.

[0003] Traditional early screening of esophageal cancer mainly relies on endoscopy and biopsy, which not only may cause discomfort, bleeding and infection risk of patients, but also is difficult to be applied to large-scale population screening due to trauma and high cost. In the field of molecular markers, the reported gene mutations (such as TP53) or protein markers have insufficient sensitivity, and the expression level in early cancer is low, resulting in high false negative rate, and some markers are prone to false positive in inflammation or other digestive diseases, and the specificity is limited. In addition, the existing non-invasive detection means has insufficient timeliness: the resolution of imaging examination (such as CT, PET-CT) for early micro lesions is low, which limits the early diagnosis window period. Although DNA methylation is an important mechanism of epigenetic regulation, its abnormality is closely related to the occurrence of esophageal cancer, but the existing researches mainly focus on the methylation site of a single gene, lack of systematic screening and large sample clinical verification of specific methylation combination markers for esophageal cancer, and cannot meet the demand of accurate screening. The above problems together lead to the difficulty of the existing technology to realize the efficient, non-invasive and high sensitivity screening of early esophageal cancer.

[0004] In view of the core defects of the existing esophageal cancer screening technology such as strong invasiveness, insufficient sensitivity and lagging detection window period, the present application proposes a set of methylation early screening markers for esophageal cancer by constructing a non-invasive detection system based on blood DNA methylation marker combination; the application of the set of markers can realize non-invasive and high-precision early screening by using targeted methylation sequencing technology to capture the methylation signal of circulating tumor DNA (ctDNA) with high sensitivity. SUMMARY

[0005] The purpose of the present application is to provide a set of methylation early screening markers for esophageal cancer and application thereof;

[0006] By detecting esophageal cancer specific multi-gene methylation combination (NOTCH1, JAG1, RUNX1, ZNF693 / CASZ1 and MMP14) in the peripheral blood of patients, quantifying the dynamic change of methylation level can effectively identify cancer patients and reduce the risk of missed detection caused by tumor heterogeneity. It overcomes the limitations of traditional technology such as insufficient sensitivity for early cancer detection and dependence on invasive operation, and can promote the clinical transformation of "early screening and early diagnosis".

[0007] In order to achieve the above purpose, the technical scheme of the present application is as follows:

[0008] The present application provides a set of esophageal cancer methylation early screening markers, characterized in that the marker is any one of ZNF693, MMP14, JAG1, RUNX1 or NOTCH1 gene sequence or gene fragment containing at least one CpG methylation site, or a combination of any two or more.

[0009] Preferably, the marker is a combination of ZNF693, MMP14, JAG1, RUNX1 and NOTCH1 gene sequence or gene fragment containing at least one CpG methylation site.

[0010] Preferably, the CpG methylation site on the ZNF217 gene is chr11: 65374866-65374867, chrll: 65374868-65374869, chrll: 65374870-65374871, chrll: 65374872-65374873, chrll: 65374874-65374875, chrll: 65374876-65374877, chrll: 65374878-65374879, chrll: 65374880-65374881, chrll: 65374882-65374883, chrll: 65374884-65374885, chrll: 65374886-65374887, chrll: 65374888-65374889, chrll: 65374889-65374890, chrll: 65374890-65374891, chrll: 65374891-65374892, chrll: 65374892-65374893, chrll: 65374893-65374894, chrll: 65374894-65374895, chrll: 65374895-65374896, chrll: 65374896-65374897, chrll: 65374897-65374898, chrll: 65374898-65374899, chrll: 65374900-65374901, chrll: 65374901-65374902, chrll: 65374902-65374903, chrll: 65374903-65374904, chrll: 65374904-65374905, chrll: 65374905-65374906, chrll: 65374906-65374907, chrll: 65374907-65374908, chrll: 65374908-65374909, chrll: 65374909-65374910, chrll: 65374910-65374911, chrll: 65374911-65374912, chrll: 65374912-65374913, chrll: 65374913-65374914, chrll: 65374914-65374915, chrll: 65374915-65374916, chrll: 65374916-65374917, chrll: 65374917-65374918, chrll: 65374918-65374919, chrll: 65374919-65374920, chrll: 65374920-65374921, chrll: 65374921-65374922, chrll: 65374922-65374923, chrll: 65374923-65374924, chrll: 65374924-65374925, chrll: 65374925-65374926, chrll: 65374926-65374927, chrll: 65374927-65374928, chrll: 65374928-65374929, chrll: 65374929-65374930, chrll: 65374930-65374931, chrll: 65374931-65374932, chrll: 65374932-65374933, chrll: 65374933-65374934, chrll: 65374934-65374935, chrll: 65374935-65374936, chrll: 65374936-65374937, chrll: 65374937-65374938, chrll: 65374938-65374939, chrll: 65374939-65374940, chrll: 65374940-65374941, chrll: 65374941-65374942,chr1:10756177-10756178, chr1:10756206-10756207, chr1:10756318-10756319, chr1:10756416-10756417, chr1:10756449-10756450, chr1:10756456-10756457, chr1:10756473-10756474, chr1:10756477-10756478, chr1:10818448-10818449, chr1:10818536-10818537, chr1:10818583-10818584, chr1:10822928-10822929, chr1:10822994-10822995, chr1:10830178-10830179, chr1:10830269-10830270, chr1:10830297-10830298, chr1:10835429-10835430, chr1:10835472-10835473, chr1:10837431-10837432, chr1:10837532-10837533, chr1:10837552-10837553, chr1:10856877-10856878, chr1:10863578-10863579, chr1:10863672-10863673, chr1:10866191-10866192, chr1:10639090-10639091, chr1:10639105-10639106, chr1:10639111-10639112, chr1:10639114-10639115, chr1:10639285-10639286, chr1:10646238-10646239, chr1:10646307-10646308, chr1:10646312-10646313, chr1:10646324-10646325, chr1:10650868-10650869, chr1:10650932-10650933, chr1:10651001-10651002, chr1:10669025-10669026, chr1:10671308-10671309, chr1:10671405-10671406, chr1:10690185-10690186, chr1:10716914-10716915, chr1:10736915-10736916,chr1: 10737202~10737203, chr1: 10756010~10756011, chr1: 10756012~1075601 3. chr1: 10756028~10756029, chr1: 10756312~10756313, chr1: 10756441~10756 442. chr1: 10756576~10756577, chr1: 10761528~10761529, chr1: 10823058~108 23059, chr1: 10830317~10830318, chr1: 10835459~10835460, chr1: 10837480~10 837481, chr1: 10837538~10837539, chr1: 10856937~10856938, chr1: 10866244~ 10866245, chr1: 10866353~10866354, chr1: 10639189~10639190, chr1: 1065114 Any one or more of the following: 2~10651143, chr1:10830300~10830301, chr1:10639037~10639038, chr1:10639055~10639056, chr1:10639264~10639265, and chr1:10639295~10639296.

[0011] Preferably, the CpG methylation sites on the ZNF693 gene are the 108 CpG methylation sites described above.

[0012] Preferably, the CpG methylation sites on the MMP14 gene are chr14: 22837821~22837822, chr14: 22837883~22837884, chr14: 22838262~22838263, chr14: 22838287~22838288, chr14: 22838534~22838535, chr1 4: Any one or more of the following: 22843476~22843477, chr14: 22852832~22852833, chr14: 22837947~22837948, chr14: 22838083~22838084, chr14: 22838122~22838123, chr14: 22838291~22838292.

[0013] Preferably, the CpG methylation sites on the MMP14 gene are the 11 CpG methylation sites described above.

[0014] Preferably, the CpG methylation site on the JAG1 gene is any one or more of chr20: 10623294-10623295, chr20: 10627195-10627196, chr20: 10637056-10637057, chr20: 10637156-10637157, chr20: 10652533-10652534, chr20: 10652637-10652638, chr20: 10665892-10665893, chr20: 10665920-10665921, chr20: 10667181-10667182, chr20: 10667188-10667189, chr20: 10667192-10667193, chr20: 10667233-10667234, chr20: 10667263-10667264, chr20: 10667273-10667274, chr20: 10667294-10667295, chr20: 10670759-10670760, chr20: 10670784-10670785, chr20: 10671542-10671543, chr20: 10637204-10637205, chr20: 10670857-10670858, chr20: 10670885-10670886, chr20: 10670925-10670926, chr20: 10671545-10671546.

[0015] Preferably, the CpG methylation site on the JAG1 gene is any one or more of the 23 CpG methylation sites described above.

[0016] Preferably, the CpG methylation site on the RUNX1 gene is any one or more of chr21:34807988-34807989, chr21:34808054-34808055, chr21:34808184-34808185, chr21:34808228-34808229, chr21:34808272-34808273, chr21:34865974-34865975, chr21:34866023-34866024, chr21:34866150-34866151, chr21:34866320-34866321, chr21:34885409-34885410, chr21:34907708-34907709, chr21:34907820-34907821, chr21:34989786-34989787, chr21:35017274-35017275, chr21:35017360-35017361, chr21:35017598-35017599, chr21:35017872-35017873, chr21:35019857-35019858, chr21:35026859-35026860, chr21:35026901-35026902, chr21:35026948-35026949, chr21:35045071-35045072, chr21:34866281-34866282, chr21:35017770-35017771, chr21:35017803-35017804, chr21:35019855-35019856, chr21:35026851-35026852, chr21:35026898-35026899, chr21:35045139-35045140, chr21:35020003-35020004.

[0017] Preferably, the CpG methylation site on the RUNX1 gene is any one or more of the 30 CpG methylation sites described above.

[0018] Preferably, the CpG methylation sites on the NOTCH1 gene chr9: 136512062-136512063, chr9: 136512085-136512086, chr9: 136512102-136512103, chr9: 136526210-136526211, chr9: 136529699-136529700, chr9: 136529806-136529807, chr9: 136529826-136529827, chr9: 136529856-136529857, chr9: 136529874-136529875, chr9: 136530469-136530470, chr9: 136530661-136530662, chr9: 136530663-136530664, chr9: 136530864-136530865, chr9: 136530984-136530985, chr9: 136538550-136538551, chr9: 136538623-136538624, chr9: 136538700-136538701, chr9: 136540156-136540157, chr9: 136540161-136540162, chr9: 136540208-136540209, chr9: 136540265-136540266, chr9: 136540288-136540289, chr9: 136540304-136540305, chr9: 136540312-136540313, chr9: 136540353-136540354, chr9: 136540393-136540394, chr9: 136512045-136512046, chr9: 136512074-136512075, chr9: 136526269-136526270, chr9: 136526398-136526399, chr9: 136526485-136526486, chr9: 136530616-136530617, chr9: 136530773-136530774, chr9: 136540133-136540134, chr9: 136542897-136542898, chr9: 136542921-136542922, chr9: 136542966-136542967, chr9: 136512176-136512177, chr9: 136526357-136526358,Any one or more of chr9: 136542962-136542963, chr9: 136512128-136512129, chr9: 136512131-136512132, chr9: 136512155-136512156.

[0019] Preferably, the CpG methylation site on the NOTCH1 gene is any one or more of the 43 CpG methylation sites described above.

[0020] The application also provides use of the above-mentioned set of esophageal cancer methylation early screening markers in the preparation of a detection kit for diagnosing and / or evaluating the methylation degree of esophageal cancer and / or in the screening of drugs for treating and / or relieving esophageal cancer.

[0021] The application also provides use of a product for detecting the above-mentioned set of esophageal cancer methylation early screening markers in the preparation of an esophageal cancer methylation degree diagnosis and / or risk prediction product.

[0022] The application has the following beneficial effects:

[0023] 1. The application discards the dependence on invasive instruments in traditional methods, adopts a non-invasive method, realizes rapid screening by detecting trace biomarkers in blood, is simple and non-invasive, avoids side effects, greatly improves patient compliance and screening applicability.

[0024] 2. The application can recognize multiple stages of early tumors by optimizing the molecular combination and algorithm analysis of methylation detection, thereby making more detailed distinctions, having multi-stage adaptability, being more advantageous in disease progression monitoring, and being able to provide more accurate grading diagnostic information for clinics. Traditional methods have very low detection rates for early lesions and high costs: for example, imaging has insufficient resolution for <5mm lesions, has a diagnostic accuracy of only about 50%-60%, and has a risk of radiation; PET-CT has high diagnostic accuracy, but is expensive and increases the economic burden of patients.

[0025] 3. Existing technology esophageal exfoliative cytology examination has better simplicity, low false positives, and can be used for large-scale screening, but the method has a sensitivity of only 46%, a specificity of 84%, and low sensitivity, and false negative results are easy to occur. More importantly, the examination is not suitable for patients with severe heart disease, hypertension, esophageal varices, and lung disease, limiting the scope of its applicable population. The application can overcome the above population restrictions and achieve more extensive applicability, especially for high-risk patient groups who cannot tolerate traditional examinations.

[0026] 4、The present application breaks through the limitations of single marker, such as unstable signal caused by tumor heterogeneity, limited detection sensitivity and specificity, and false positive caused by abnormal expression of markers induced by inflammation or other non-cancer diseases. The results of the model constructed by the present application are as follows: the specificity and sensitivity in the verification set are close to 90%; the specificity in the training set is >91%, and the sensitivity is >97%, which indicates that higher detection rate can be achieved in precancerous lesions and early cancer stages, and the risk of false negative and false positive is effectively reduced, providing a reliable basis for early screening. More importantly, while ensuring high accuracy, the present application only relies on 215 detection sites to complete the detection, greatly reducing the detection cost, and realizing the dual optimization of detection efficiency and economic cost. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 is the AUC value corresponding to the candidate gene site;

[0028] Figure 2 is the sensitivity and specificity of 215 sites in the binary classification model in ESCC;

[0029] Figure 3 is the sensitivity and specificity of 215 sites in the binary classification model in different stages of ESCC. DETAILED DESCRIPTION

[0030] The experimental methods used in the following examples are conventional methods unless otherwise specified.

[0031] The materials, reagents, etc. used in the following examples can be obtained from commercial channels unless otherwise specified.

[0032] In order to make the purpose, technical scheme and advantages of the present application clearer, further detailed description will be made below in combination with specific examples. It should be understood that the specific examples described herein are only used to explain the present application, and are not used to limit the present application.

[0033] Example 1 Methylation sequencing and analysis

[0034] 1. Experimental materials

[0035] 1.1 Tissue samples: including esophageal squamous cell carcinoma tissues (205 cases), cancer-adjacent normal tissues (201 cases), high-grade intraepithelial neoplasia (20 cases), and low-grade intraepithelial neoplasia (13 cases).

[0036] 1.2 Plasma samples: cancer patient plasma (204 cases) and non-cancer control plasma (814 cases).

[0037] The experiments involved in the present application have been approved by the National Cancer Center / Chinese Academy of Medical Sciences, Peking Union Medical College, and each patient has signed an informed consent form.

[0038] 2. Experimental methods and results

[0039] 1) Whole genome bisulfite sequencing (WGBS) was used to identify differentially methylated regions (DMRs) in tissues (esophageal squamous cell carcinoma tissues, adjacent normal tissues, high-grade intraepithelial neoplasia, and low-grade intraepithelial neoplasia) and circulating cell-free DNA (cfDNA) using metilene. Wilcox was used to calculate the significance of each CpG site in the esophageal squamous cell carcinoma group compared with the adjacent normal group, high-grade intraepithelial neoplasia group, and low-grade intraepithelial neoplasia group, respectively.

[0040] 2) In the union set of DMRs in tissues and cfDNA, CpG sites with significant differences between the esophageal squamous cell carcinoma group and the healthy control group were screened using Wilcox test, and the distribution difference of the sites was evaluated according to the interquartile range (Q1-Q3).

[0041] 3) Based on the significantly different sites in the union set of DMRs in tissues and cfDNA, targeted probes were designed to assist in the design of targeted methylation sequencing. A total of 290,297 CpG sites were captured by the probes.

[0042] Example 2: Screening of markers

[0043] 1. Experimental materials and methods

[0044] 1.1 Plasma samples

[0045] Esophageal squamous cell carcinoma group: 530 cases of cancer plasma; healthy control group: 1212 cases of non-cancer plasma.

[0046] 1.2 Experimental methods

[0047] Targeted methylation sequencing was performed on the plasma samples in this example using the probes designed in Example 1, and the methylation values of a total of 290,297 CpG sites were captured.

[0048] 1.3 Screening criteria for markers

[0049] In order to determine the prediction markers with high prediction performance, the following screening methods were used:

[0050] ① Statistical method: Wilcoxon rank sum test was used to calculate whether the difference of each CpG site in the plasma sample between the esophageal squamous cell carcinoma group and the healthy control group was significant.

[0051] ② Gene level evaluation: Each CpG site was annotated to a gene according to the nearest tss, and all significant difference sites in each gene in the training set were included in the SVM model for training, and the AUC of the model in the validation set was calculated.

[0052] 2. Experimental results

[0053] As shown in Table 1 and Figure 1 Table 2, the invasive cells are the most in esophageal squamous cell carcinoma and the methylation changes are the most intense, among which the top 5 genes ranked by AUC are ZNF693 (CASZ1), MMP14, JAG1, RUNX1 and NOTCH1, and the AUC values are all greater than 0.8, which can be used as biomarkers for early methylation screening of esophageal cancer. Combined with the difference degree of binding sites (BH corrected p value, Q1-Q3) and the biological role of the genes, 215 CpG sites located on ZNF693 / MMP14 / JAG1 / RUNX1 / NOTCH1 are finally determined as biomarkers for early methylation screening of esophageal cancer, and the gene position information of the 215 sites is shown in Table 3.

[0054] Table 1 AUC value results of genes

[0055]

[0056] Table 2 AUC value results of single sites and combined sites of top 5 genes

[0057]

[0058]

[0059] Table 3 Gene position information table of 215 CpG sites

[0060]

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[0065]

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[0070]

[0071] Note: Distal Intergenic means distant intergenic region, Exon means exon, Intron means intron, Promoter means promoter, 3'UTR means 3' untranslated region, Downstream means downstream region.

[0072] Table 4. Statistics on the number of loci on each gene

[0073] Gene ZNF693 NOTCH1 JAG1 MMP14 RUNX1 Number of sites 108 43 23 11 30

[0074] Example 3: Model Training and Validation of Biomarker Combinations

[0075] 1. Experimental Methods

[0076] 1) Model training: Using cfDNA methylation data from 734 cancer plasma samples and 2026 non-cancer plasma samples, methylation information of 215 sites identified in Example 2 was extracted, and a binary classification model to distinguish between cancer and non-cancer samples was established using a machine learning SVM model.

[0077] 2) Model validation: Methylation information of 215 sites identified in Example 2 was extracted from cfDNA methylation data of 112 cancer patients and 285 non-cancer patients. The binary classification model trained in step 1) was used to predict whether the participants belonged to cancer or non-cancer groups. The results were compared with the actual grouping of the participants, and the sensitivity and specificity were calculated.

[0078] 3) Patients were classified into stages 1-4 according to TNM staging, and the sensitivity and specificity of the model established at 215 sites in different stages of ESCC were evaluated.

[0079] 2. Experimental Results

[0080] like Figure 2 As shown in Table 5, the binary classification model constructed using the training set of this invention for esophageal cancer prediction showed an AUC of 0.96 on the validation set, with a specificity of 88.07% and a sensitivity of 89.29%; the AUC on the training set was 0.98, with a specificity of 91.96% and a sensitivity of 97.96%. This indicates that a combination of 215 loci can serve as a biomarker, and its diagnostic effect is significantly better than that of a single gene. As shown in Table 2 of Example 2, single loci of the top 5 genes and combinations of multiple loci on the top 5 genes can also serve as biomarkers for early esophageal cancer screening.

[0081] Table 5. Statistical analysis of the specificity and sensitivity of diagnoses based on the training and validation sets of the binary classification model.

[0082]

[0083] In addition, by Figure 3As shown, based on the binary classification model, in different stages of ESCC, the sensitivity of the validation set is close to 0.7 except for stage II, and the sensitivity of stages I, III and IV is greater than 0.85, and the sensitivity of the whole stage is close to 0.9; The sensitivity of I, II, III and IV stages and the whole stage in the training set is high, all greater than 0.9. It is shown that the combination of 215 sites as biomarkers can identify multiple stages of early esophageal cancer tumors and can be more detailed, and has multi-stage adaptability.

[0084] In summary, the biomarker provided by the present application can effectively overcome the multiple limitations of traditional esophageal cancer detection methods in terms of invasiveness, accuracy, early detection ability and resource consumption. It has the characteristics of non-invasiveness, high sensitivity and specificity, multi-marker integration and large-scale screening applicability, etc., and provides a new solution for early screening and accurate diagnosis of esophageal cancer. Especially in the environment of limited medical resources at the grassroots level, it can significantly improve the screening efficiency and coverage, and provide important support for improving the early detection rate and cure rate of esophageal cancer, and has significant clinical application value and popularization prospect.

[0085] The above-described embodiments only express the preferred embodiments of the present application, and the description is more specific and detailed, but it should not be understood as limiting the scope of the present application. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of protection of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A panel of methylation early screening markers for esophageal cancer, characterized in that, the marker is a combination of ZNF693, MMP14, JAG1, RUNX1 and NOTCH1 gene sequences or gene fragments containing 215 CpG methylation sites; The ZNF693 gene on the site of CpG methylation is 108, chr1: 10756170 ~ 10756171, chr1: 10756177 ~ 10756178, chr1: 10756206 ~ 10756207, chr1: 10756416 ~ 10756417, chr1: 10856937 ~ 10856938, chr1: 10635542 ~ 10635543, chr1: 10635544 ~ 10635545, chr1: 10635546 ~ 10635547, chr1: 10635549 ~ 10635550, chr1: 10635566 ~ 10635567, chr1: 10635601 ~ 10635602, chr1: 10635628 ~ 10635629, chr1: 10635659 ~ 10635660, chr1: 10635689 ~ 10635690, chr1: 10635696 ~ 10635697, chr1: 10635703 ~ 10635704, chr1: 10635741 ~ 10635742, chr1: 10635785 ~ 10635786, chr1: 10638995 ~ 10638996, chr1: 10639031 ~ 10639032, chr1: 10639064 ~ 10639065, chr1: 10639087 ~ 10639088, chr1: 10639102 ~ 1063910 chr1: 10639117 ~ 10639118, chr1: 10639126 ~ 10639127, chr1: 10639144 ~ 10639145, chr1: 10639219 ~ 10639220, chr1: 10668717 ~ 10668718, chr1: 10669030 ~ 10669031, chr1: 10671192 ~ 10671193, chr1: 10671310 ~ 10671311, chr1: 10671328 ~ 10671329, chr1: 10690021 ~ 10690022, chr1: 10699813 ~ 10699814, chr1: 10716943 ~ 10716944, chr1: 10717003 ~ 10717004, chr1: 10736799 ~ 10736800, chr1: 10736829 ~ 10736830, chr1: 10736891 ~ 10736892, chr1: 10737169 ~ 10737170, chr1: 10737296 ~ 10737297, chr1: 10737392 ~ 10737393,chr1: 10755964~10755965, chr1: 10755988~10755989, chr1: 10756112~10756113, chr1: 10756119~10756120, chr1: 10756318~10756319, chr1: 10756449~10756450, chr1: 10756456~10756457, chr1: 10756473~10756474, chr1: 10756477~10756478, chr1: 10818448~10818449, chr1: 10818536~10818537, chr1: 10818583~10818584, chr1: 10822928~10822929, chr1: 10822994~10822995, chr1: 10830178~10830179, chr1: 10830269~10830270, chr1: 10830297~10830298, chr1: 10835429~10835430, chr1: 10835472~10835473, chr1: 10837431~10837432, chr1: 10837532~10837533, chr1: 10837552~10837553, chr1: 10856877~10856878, chr1: 10863578~10863579, chr1: 10863672~10863673, chr1: 10866191~10866192, chr1: 10639090~10639091, chr1: 10639105~10639106, chr1: 10639111~10639112, chr1: 10639114~10639115, chr1: 10639285~10639286, chr1: 10646238~10646239, chr1: 10646307~10646308, chr1: 10646312~10646313, chr1: 10646324~10646325, chr1: 10650868~10650869, chr1: 10650932~10650933, chr1: 10651001~10651002, chr1: 10669025~10669026, chr1: 10671308~10671309, chr1: 10671405~10671406, chr1: 10690185~10690186, chr1: 10716914~10716915,chr1: 10736915~10736916, chr1: 10737202~10737203, chr1: 10756010~10756011, chr1: 10756012~10756013, chr1: 10756028~10756029, chr1: 10756312~10756313, chr1: 10756441~10756442, chr1: 10756576~10756577, chr1: 10761528~10761529, chr1: 10823058~10823059, chr1: 10830317~10830318, chr1: 10835459~10835460, chr1: 10837480~10837481, chr1: 10837538~10837539, chr1: 10866244~10866245, chr1: 10866353~10866354, chr1: 10639189~10639190, chr1: 10651142~10651143, chr1: 10830300~10830301, chr1: 10639037~10639038, chr1: 10639055~10639056, chr1: 10639264~10639265, and chr1: 10639295~10639296; the 11 CpG methylation sites on the MMP14 gene are chr14:22837821-22837822, chr14:22837883-22837884, chr14:22838262-22838263, chr14:22838287-22838288, chr14:22838291-22838292, chr14:22838534-22838535, chr14:22843476-22843477, chr14:22852832-22852833, chr14:22837947-22837948, chr14:22838083-22838084 and chr14:22838122-22838123; the 23 CpG methylation sites on the JAG1 gene are chr20:10667192-10667193, chr20:10670759-10670760, chr20:10670784-10670785, chr20:10671542-10671543, chr20:10670857-10670858, chr20:10623294-10623295, chr20:10627195-10627196, chr20:10637056-10637057, chr20:10637156-10637157, chr20:10652533-10652534, chr20:10652637-10652638, chr20:10665892-10665893, chr20:10665920-10665921, chr20:10667181-10667182, chr20:10667188-10667189, chr20:10667233-10667234, chr20:10667263-10667264, chr20:10667273-10667274, chr20:10667294-10667295, chr20:10637204-10637205, chr20:10670885-10670886, chr20:10670925-10670926 and chr20:10671545-10671546; The CpG methylation sites on the RUNX1 gene are 30, which are chr21: 34907820~34907821, chr21: 35026859~35026860, chr21: 35026948~35026949, chr21: 35026851~35026852, chr21: 35026898~35026899, chr21: 34807988~34807989, chr21: 34808054~34808055, chr21: 34808184~34808185, chr21: 34808228~34808229, chr21: 34808272~34808273, chr21: 34865974~34865975, chr21: 34866023~34866024, chr21: 34866150~34866151, chr21: 34866320~34866321, chr21: 34885409~34885410, chr21: 34907708~34907709, chr21: 34989786~34989787, chr21: 35017274~35017275, chr21: 35017360~35017361, chr21: 35017598~35017599, chr21: 35017872~35017873, chr21: 35019857~35019858, chr21: 35026901~35026902, chr21: 35045071~35045072, chr21: 34866281~34866282, chr21: 35017770~35017771, chr21: 35017803~35017804, chr21: 35019855~35019856, chr21: 35045139~35045140 and chr21: 35020003~35020004; The CpG methylation sites on the NOTCH1 gene are 43, which are chr9: 136529806~136529807, chr9: 136538700~136538701, chr9: 136540265~136540266, chr9: 136540304~136540305, chr9: 136540312~136540313, chr9: 136512062~136512063, chr9: 136512085~136512086, chr9: 136512102~136512103, chr9: 136526210~136526211, chr9: 136529699~136529700, chr9: 136529826~136529827, chr9: 136529856~136529857, chr9: 136529874~136529875, chr9: 136530469~136530470, chr9: 136530661~136530662, chr9: 136530663~136530664, chr9: 136530864~136530865, chr9: 136530984~136530985, chr9: 136538550~136538551, chr9: 136538623~136538624, chr9: 136540156~136540157, chr9: 136540161~136540162, chr9: 136540208~136540209, chr9: 136540288~136540289, chr9: 136540353~136540354, chr9: 136540393~136540394, chr9: 136512045~136512046, chr9: 136512074~136512075, chr9: 136526269~136526270, chr9: 136526398~136526399, chr9: 136526485~136526486, chr9: 136530616~136530617, chr9: 136530773~136530774, chr9: 136540133~136540134, chr9: 136542897~136542898, chr9: 136542921~136542922, chr9: 136542966~136542967.chr9: 136512176-136512177, chr9: 136526357-136526358, chr9: 136542962- 136542963, chr9: 136512128-136512129, chr9: 136512131-136512132, and chr9: 136512155-136512156.

2. The use of a reagent for detecting the methylation expression level of the set of esophageal cancer methylation early screening markers according to claim 1 in the preparation of a detection kit for diagnosing and / or evaluating the methylation degree of esophageal cancer.

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